Comparison of sequencing-based and array-based genotyping platforms for genomic prediction of maize hybrid performance

Genomic selection(GS)is a powerful tool for improving genetic gain in maize breeding.However,its routine application in large-scale breeding pipelines is limited by the high cost of genotyping platforms.Although sequencing-based and array-based genotyping platforms have been used for GS,few studies have compared prediction performance among platforms.In this study,we evaluated the predictabilities of four agronomic traits in 305 maize hybrids derived from 149 parental lines subjected to genotyping by sequencing(GBS),a 40K SNP array,and target sequence capture(TSC)using eight GS models.The GBS marker dataset yielded the highest predictabilities for all traits,followed by TSC and SNP array datasets.We investigated the effect of marker density and statistical models on predictability among genotyping platforms and found that 1K SNPs were sufficient to achieve comparable predictabilities to 10K and all SNPs,and BayesB,GBLUP,and RKHS performed well,while XGBoost performed poorly in most cases.We also selected significant SNP subsets using genome-wide association study(GWAS)analyses in three panels to predict hybrid performance.GWAS facilitated selecting effective SNP subsets for GS and thus reduced genotyping cost,but depended heavily on the GWAS panel.We conclude that there is still room for optimization of the existing SNP array,and using genotyping by target sequencing(GBTS)techniques to integrate a few functional markers identified by GWAS into the 1K SNP array holds great promise of being an effective strategy for developing desirable GS breeding arrays.

Research Progress of Anaerobic Digestion Pretreatment of Antibiotic Waste

Anaerobic digestion is one of the effective ways to dispose of antibiotic pharmaceutical waste. However,the inhibition of antibiotics on anaerobic fermentation microorganisms seriously hinders the normal physiological activities of anaerobic microorganisms and then affects the efficiency of anaerobic digestion. In order to solve this problem,related scholars have done a lot of research. It has been found that pretreatment of anaerobic microorganisms and antibiotic pharmaceutical waste can significantly improve the efficiency of anaerobic digestion. In this paper,the current feasible pretreatment methods were summarized,and the application of different pretreatment methods was analyzed to provide reference for improving pretreatment methods and improving anaerobic biological treatment ability of antibiotic waste.

The landscape of gene-CDS-haplotype diversity in rice:Properties,population organization,footprints of domestication and breeding,and implications for genetic improvement

Polymorphisms within gene coding regions represent the most important part of the overall genetic diversity of rice.We characterized the gene-coding sequence-haplotype(gcHap)diversity of 45963 rice genes in 3010 rice accessions.With an average of 226±390 gcHaps per gene in rice populations,rice genes could be classified into three main categories:12865 conserved genes,10254 subspecific differentiating genes,and 22844 remaining genes.We found that 39218 rice genes carry>255179 major gcHaps of potential functional importance.Most(87.5%)of the detected gcHaps were specific to subspecies or populations.The inferred proto-ancestors of local landrace populations reconstructed from conserved predominant(ancient)gcHaps correlated strongly with wild rice accessions from the same geographic regions,supporting a multiorigin(domestication)model of Oryza sativa.Past breeding efforts generally increased the gcHap diversity of modern varieties and'caused significant frequency shifts in predominant gcHaps of 14266 genes due to independent selection in the two subspecies.Low frequencies of“favorable”gcHaps at most known genes related to rice yield in modern varieties suggest huge potential for rice improvement by mining and pyramiding of favorable gcHaps.The gcHap data were demonstrated to have greater power than SNPs for the detection of causal genes that affect complex traits.The rice gcHap diversity dataset generated in this study would facilitate rice basic research and improvement in the future.

Mapping quantitative trait loci using binned genotypes

Precise mapping of quantitative trait loci(QTLs)is critical for assessing genetic effects and identifying candidate genes for quantitative traits.Interval and composite interval mappings have been the methods of choice for several decades,which have provided tools for identifying genomic regions harboring causal genes for quantitative traits.Historically,the concept was developed on the basis of sparse marker maps where genotypes of loci within intervals could not be observed.Currently,genomes of many organisms have been saturated with markers due to the new sequencing technologies.Genotyping by sequencing usually generates hundreds of thousands of single nucleotide polymorphisms(SNPs),which often include the causal polymorphisms.The concept of interval no longer exists,prompting the necessity of a norm change in QTL mapping technology to make use of the high-volume genomic data.Here we developed a statistical method and a software package to map QTLs by binning markers into haplotype blocks,called bins.The new method detects associations of bins with quantitative traits.It borrows the mixed model methodology with a polygenic control from genome-wide association studies(GWAS)and can handle all kinds of experimental populations under the linear mixed model(LMM)framework.We tested the method using both simulated data and data from populations of rice.The results showed that this method has higher power than the current methods.An R package named binQTL is available from GitHub.